Upload 51 files

requirements.txt

@@ -12,5 +12,4 @@ google-auth-httplib2==0.2.0
 google-api-core==2.23.0
 googleapis-common-protos==1.66.0
 python-dotenv==1.0.1
-tensorflow==2.15.1
-tensorflow_hub==0.16.1
+nltk

youtube.py

@@ -1,12 +1,7 @@
-from helper import extract_youtube_id, get_all_comments, text_preprocessing, slang_dict
+from helper import extract_youtube_id, get_all_comments
 import streamlit as st
 import random
 import pandas as pd
-import tensorflow as tf
-import tensorflow_hub as tf_hub
-
-def load_model():
-  return tf.keras.models.load_model('model_lstm.h5', custom_objects={'KerasLayer': tf_hub.KerasLayer})
 
 st.header("💬 Youtube Comments Sentiment Analysis")
 
@@ -35,11 +30,7 @@ if st.button('Submit', type="secondary"):
     the_youtube_id = extract_youtube_id(user_input)
     if the_youtube_id:
       with st.spinner("Please wait while we're loading the data..."):
-        the_data, comment_only = get_all_comments(the_youtube_id)
-        the_comments = pd.DataFrame({"comment": comment_only})
-        the_comments['text_processed'] = the_comments['comment'].apply(lambda x: text_preprocessing(x))
-        the_comments['text_processed'] = the_comments['text_processed'].apply(lambda x: x if len(x.split()) > 0 else None)
-        the_comments = the_comments.dropna(subset=['text_processed'])
+        the_data = get_all_comments(the_youtube_id)
         st.balloons()
         st.markdown(f"""<p style="color: green; padding: 0; margin: 0;">Total comments: {len(the_data)}</p>""", unsafe_allow_html=True)
         for data in the_data:
@@ -50,7 +41,7 @@ if st.button('Submit', type="secondary"):
                 <p style="font-size: 18px; line-height: 1.6; color: #333; font-family: 'Arial', sans-serif;">
                   {data["comment"]}
                 </p>
-                <p style="font-size: 16px; color: gray; margin-top: 15px; font-family: 'Arial', sans-serif; font-weight: bold;">
+                <p style="font-size: 16px; margin-top: 15px; font-family: 'Arial', sans-serif; font-weight: bold;">
                   Sentiment Analysis: <span style="color: {sentiment_color}; font-size: 18px; font-weight: bold; padding: 5px 10px; background-color: {sentiment_color + "33"}; border-radius: 5px;">
                     {sentiment}
                   </span>